超像素分割, 并获取每一个分区

参考、学习自Greatpyimagesearch

from skimage.segmentation import slic
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
import matplotlib.pyplot as plt
import numpy as np
import cv2


# args
args = {"image": './hand_0.png'}

# load the image and apply SLIC and extract (approximately)
# the supplied number of segments
image = cv2.imread(args["image"])
segments = slic(img_as_float(image), n_segments=100, sigma=5)

# show the output of SLIC
fig = plt.figure('Superpixels')
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments))
plt.axis("off")
plt.show()
print("segments:\n", segments)
print("np.unique(segments):", np.unique(segments))
# loop over the unique segment values
for (i, segVal) in enumerate(np.unique(segments)):
    # construct a mask for the segment
    print("[x] inspecting segment {}, for {}".format(i, segVal))
    mask = np.zeros(image.shape[:2], dtype="uint8")
    mask[segments == segVal] = 255

    # show the masked region
    cv2.imshow("Mask", mask)
    cv2.imshow("Applied", np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0))
    cv2.waitKey(0)